PCA Features

Before running the principal components analysis, it was necessary to first determine the number of PC’s required to account for 80% of the variance, a machine-learning algorithmm benchmark that provides sufficient confidence in the analysis.

#Plot Features of the PCA
library(readxl)
library(dplyr)
library(plotly)
#transpose the dataset (required for PCA)
data.pca<-t(LFQs.Kidney_raw)
data.pca<-as.data.frame(data.pca)
##Import the data to be used for annotation
Index<-c(0,0,0,0,1,1,1,1)
Index<-as.data.frame(Index)
##merge the file
data.pca_Final<-cbind(Index, data.pca)
rownames(data.pca_Final)<-data.pca_Final$Row.names
pca.comp<-prcomp(data.pca_Final[,(ncol(Index)+2):ncol(data.pca_Final)])

pcaCharts=function(x) {
    x.var <- x$sdev ^ 2
    x.pvar <- x.var/sum(x.var)
    par(mfrow=c(2,2))
    plot(x.pvar,xlab="Principal component", 
         ylab="Proportion of variance", ylim=c(0,1), type='b')
    plot(cumsum(x.pvar),xlab="Principal component", 
         ylab="Cumulative Proportion of variance", 
         ylim=c(0,1), 
         type='b')
    screeplot(x)
    screeplot(x,type="l")
    par(mfrow=c(1,1))
}
pcaCharts(pca.comp)

png(file=paste0("../2_Output/2_Protein/Proteomics.Kidney_PCA.Charts.png"))
pcaCharts(pca.comp)
dev.off()

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3-Dimensional PCA

From the previous calculations, it is appears that 3 principal components are necessary (accounting for >80% cumulative variance).

##Create a 3D-PCA for Inspection
library(plotly)
##Index
PCs<-cbind(pca.comp$x, Index)
rownames(PCs)<-rownames(data.pca)
ax_text<-list(
  family = "times",
  size = 12,
  color = "black")
t <- list(
  family = "times",
  size = 14,
  color = "black")
p <- plot_ly(PCs, x = ~PC1, y = ~PC2, z = ~PC3,
   marker = list(color = ~Index, 
                 colorscale = c('#FFE1A1', '#683531'), 
                 showscale = TRUE),
  text=rownames(PCs)) %>%
  add_markers() %>% 
  layout(scene = list(
     xaxis = list(title = 'PC1', zerolinewidth = 4, 
        zerolinecolor="darkgrey", linecolor="darkgrey", 
        linewidth=4, titlefont=t, tickfont=ax_text),
     yaxis = list(title = 'PC2', zerolinewidth = 4, 
        zerolinecolor="darkgrey", linecolor="darkgrey", 
        linewidth=4, titlefont=t, tickfont=ax_text),
    zaxis = list(title = 'PC3', zerolinewidth = 4, 
        zerolinecolor="darkgrey",  linecolor="darkgrey", 
        linewidth=4, titlefont=t, tickfont=ax_text)),
  annotations = list(
           x = 1.13,
           y = 1.03,
           text = 'Diabetes',
           xref = '1',
           yref = '0',
           showarrow = FALSE))
p #must comment out for PDF generation via knitr (Pandoc)

library(pheatmap)
library(dplyr)

Results_HM<-dplyr::filter(Kidney_raw.complete.cleaned, minuslog_pval>2)
HM_data.p05<-data.matrix(dplyr::select(Results_HM, contains("LFQ")))
rownames(HM_data.p05)<-Results_HM$Gene.Symbol
#Import the Index File
paletteLength <- 100
myColor <- colorRampPalette(c("dodgerblue4", "white", "brown4"))(paletteLength)
pheatmap(HM_data.p05, color = myColor, scale = "row")

pheatmap(HM_data.p05, color = myColor, scale = "row", filename = "../2_Output/2_Protein/Heatmap.Kidney_LFQs.p01.pdf")

##Volcano Plot

# Load packages
library(dplyr)
library(ggplot2)
library(ggrepel)
# Read data from the web
results<-read.xlsx("../1_Input/2_Protein/Kidney - PN-0064-3 Ergebnisse - mep.xlsx", sheet = "IPA_Import")
results = mutate(results, sig=ifelse(results$minuslogpval>1.3 & abs(results$log2FC)>0.585, "p < 0.05 and |FC| > 1.5", "Not Sig"))
results<-dplyr::arrange(results, desc(sig), desc(abs(log2FC)))
LABEL=results$Gene[1:20]
#plot the ggplot
p = ggplot(results, aes(log2FC, minuslogpval)) + theme(panel.background = element_rect("white", colour = "black", size=2), panel.grid.major = element_line(colour = "gray50", size=.75), panel.grid.minor = element_line(colour = "gray50", size=0.4)) + 
geom_point(aes(fill=sig), colour="black", shape=21) + labs(x=expression(Log[2](Fold-Change)), y=expression(-Log[10](P-value))) + xlim(-5,5)+ geom_hline(yintercept = 0, size = 1) + geom_vline(xintercept=0, size=1)+ 
scale_fill_manual(values=c("black", "tomato"))
#add a repelling effect to the text labels.
p+geom_text_repel(data=filter(results, minuslogpval>6 & abs(log2FC) > 1 | abs(log2FC) > 3 & minuslogpval>2), aes(label=Gene))

pdf(file = "../2_Output/2_Protein/Volcano.Plot_Kidney.pdf", width = 5.3, height = 4.5)
p+geom_text_repel(data=filter(results, minuslogpval>7.5 | abs(log2FC) > 3 & minuslogpval>3), aes(label=Gene))
dev.off()

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##Weighted Gene Co-expression Network Analysis (WGCNA)